Dr Spyros Samothrakis
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Email
ssamot@essex.ac.uk -
Telephone
+44 (0) 1206 872683
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Location
parkside block c2, Colchester Campus
Profile
Qualifications
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2014, PhD Computer Science,University of Essex
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2007, MSc Intelligent Systems, University of Sussex
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2003, BSc Computer Science, University of Sheffield
Research and professional activities
Research interests
Reinforcement Learning
Machine Learning
Neural Networks
Role Playing Games
Teaching and supervision
Current teaching responsibilities
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Navigating the Digital World (CS220)
Current supervision
Previous supervision
Degree type: Master of Science
Awarded date: 5/10/2016
Publications
Journal articles (16)
Dwivedi, YK., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, PV., Janssen, M., Jones, P., Kar, AK., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, LC., Misra, S., Mogaji, E., Sharma, SK., Singh, JB., Raghavan, V., Raman, R., Rana, NP., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P. and Williams, MD., (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 101994-101994
Samothrakis, S., (2018). Viewpoint: Artificial intelligence and labour. IJCAI International Joint Conference on Artificial Intelligence. 2018-July, 5652-5655
Samothrakis, S., (2018). Kathryn E. Merrick: Computational models of motivation for game-playing agents: Springer, 2016, 213 pp, ISBN: 978-3-319-33457-8. Genetic Programming and Evolvable Machines. 19 (4), 567-568
Samothrakis, S., (2018). Kathryn E. Merrick: Computational models of motivation for game-playing agents - Springer, 2016, 213 pp, ISBN: 978-3-319-33457-8.. Genetic Programming and Evolvable Machines. 19, 567-568
Samothrakis, S., Fasli, M., Perez, D. and Lucas, S., (2017). Default policies for global optimisation of noisy functions with severe noise. Journal of Global Optimization. 67 (4), 893-907
Tom Vodopivec, Samothrakis, S. and Brank Ster, (2017). On monte carlo tree search and reinforcement learning. The Journal of Artificial Intelligence Research. 60, 881-936
Samothrakis, S., Perez, D., Lucas, SM. and Rohlfshagen, P., (2016). Predicting Dominance Rankings for Score-Based Games. IEEE Transactions on Computational Intelligence and AI in Games. 8 (1), 1-12
Perez-Liebana, D., Samothrakis, S., Togelius, J., Schaul, T., Lucas, SM., Couetoux, A., Lee, J., Lim, C-U. and Thompson, T., (2016). The 2014 General Video Game Playing Competition. IEEE Transactions on Computational Intelligence and AI in Games. 8 (3), 229-243
Samothrakis, S. and Fasli, M., (2015). Emotional Sentence Annotation Helps Predict Fiction Genre. PLOS ONE. 10 (11), e0141922-e0141922
Perez, D., Mostaghim, S., Samothrakis, S. and Lucas, SM., (2015). Multiobjective Monte Carlo Tree Search for Real-Time Games. IEEE Transactions on Computational Intelligence and AI in Games. 7 (4), 347-360
Perez, D., Powley, EJ., Whitehouse, D., Rohlfshagen, P., Samothrakis, S., Cowling, PI. and Lucas, SM., (2014). Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions. IEEE Transactions on Computational Intelligence and AI in Games. 6 (1), 31-45
Perez, D., Togelius, J., Samothrakis, S., Rohlfshagen, P. and Lucas, SM., (2014). Automated Map Generation for the Physical Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation. 18 (5), 708-720
Samothrakis, S., Lucas, S., Runarsson, T. and Robles, D., (2013). Coevolving Game-Playing Agents: Measuring Performance and Intransitivities. IEEE Transactions on Evolutionary Computation. 17 (2), 213-226
Friston, K., Samothrakis, S. and Montague, R., (2012). Active inference and agency: optimal control without cost functions. Biological Cybernetics. 106 (8-9), 523-541
Browne, CB., Powley, E., Whitehouse, D., Lucas, SM., Cowling, PI., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S. and Colton, S., (2012). A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games. 4 (1), 1-43
Samothrakis, S., Robles, D. and Lucas, S., (2011). Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man. IEEE Transactions on Computational Intelligence and AI in Games. 3 (2), 142-154
Conferences (24)
Alshahrani, M., Samothrakis, S. and Fasli, M., (2019). Identifying idealised vectors for emotion detection using CMA-ES
Raza, H. and Samothrakis, S., (2019). Bagging Adversarial Neural Networks for Domain Adaptation in Non-Stationary EEG
Rajalingam, VR. and Samothrakis, S., (2019). Neuroevolution Strategies for Word Embedding Adaptation in Text Adventure Games
Hernandez, D., Denamganai, K., Gao, Y., York, P., Devlin, S., Samothrakis, S. and Walker, JA., (2019). A Generalized Framework for Self-Play Training
Samothrakis, S., (2018). Viewpoint: Artificial Intelligence and Labour.
Samothrakis, S., Vodopivec, T., Fairbank, M. and Fasli, M., (2017). Convolutional-Match Networks for Question Answering
Alshahrani, M., Samothrakis, S. and Fasli, M., (2017). Word mover's distance for affect detection
Abdullahi, U., Samothrakis, S. and Fasli, M., (2017). Counterfactual domain adversarial training of neural networks
Abdullahi, UI., Samothrakis, S., Fasli, M. and IEEE, (2017). Counterfactual Domain Adversarial Training of Neural Networks
Alshahrani, M., Samothrakis, S., Fasli, M. and IEEE, (2017). Word Mover's Distance for Affect Detection
Perez-Liebana, D., Samothrakis, S., Togelius, J., Lucas, SM. and Schaul, T., (2016). General video game AI: Competition, challenges, and opportunities
Samothrakis, S., Vodopivec, T., Fasli, M. and Fairbank, M., (2016). Match memory recurrent networks
Perez-Liebana, D., Samothrakis, S., Togelius, J., Schaul, T. and Lucas, SM., (2016). Analyzing the robustness of general video game playing agents
Samothrakis, S., Perez-Liebana, D., Lucas, SM. and Fasli, M., (2015). Neuroevolution for General Video Game Playing
Lucas, SM., Samothrakis, S. and Pérez, D., (2014). Fast Evolutionary Adaptation for Monte Carlo Tree Search
Perez, D., Powley, E., Whitehouse, D., Samothrakis, S., Lucas, S. and Cowling, PI., (2014). The 2013 Multi-objective Physical Travelling Salesman Problem Competition
Perez, D., Samothrakis, S. and Lucas, S., (2014). Knowledge-based fast evolutionary MCTS for general video game playing
Samothrakis, S., Roberts, SA., Perez, D. and Lucas, SM., (2014). Rolling horizon methods for games with continuous states and actions
Perez, D., Samothrakis, S., Lucas, S. and Rohlfshagen, P., (2013). Rolling horizon evolution versus tree search for navigation in single-player real-time games
Perez, D., Samothrakis, S. and Lucas, S., (2013). Online and offline learning in multi-objective Monte Carlo Tree Search
Ashlock, D., Ashlock, W., Samothrakis, S., Lucas, S. and Lee, C., (2012). From competition to cooperation: Co-evolution in a rewards continuum
Samothrakis, S. and Lucas, S., (2011). Approximating n-player behavioural strategy nash equilibria using coevolution
Samothrakis, S., Rob, D. and Lucas, SM., (2010). A UCT agent for Tron: Initial investigations
Samothrakis, S. and Lucas, SM., (2010). Planning using online evolutionary overfitting
Grants and funding
2019
The Research Centre on Micro-Social Change (MiSoC)
Economic and Social Research Council
The development of a new CPD tracker using AI and embedded machine learning to track and enhance performance of all staff.
Innovate UK (formerly Technology Strategy Board)
Orbital Media IV (EIRA)
Orbital Media & Advertising Ltd
2018
Discovering Individual and Social Preferences through Inverse Reinforcement Learning
Economic and Social Research Council
Develop AI methods to optimise interactions with customers.
Innovate UK (formerly Technology Strategy Board)
2017
The project investigates the use of algorithms (genetic + reinforcement) to provide accurate forecasts of asset prices.
Innovate UK (formerly Technology Strategy Board)
Embedding a Machine Learning capability into the Hood Group Ltd platform.
Innovate UK (formerly Technology Strategy Board)
IAA ECC Challenge Lab project - Community inclusion
Catalyst Project (HEFCE Funding)
Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.
Prequin
Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.
Prequin
The project investigates the use of algorithms (genetic + reinforcement) to provide accurate forecasts of asset prices.
Innovate UK (formerly Technology Strategy Board)
To embed a NLP capability in Objective IT
Innovate UK (formerly Technology Strategy Board)
2016
67% Embedding an innovative application of advanced data mining, data analytics and data visualisation to exploit the growth potential of the UK's leading insight platform for professional services firms
Technology STrategy Board
33% Embedding an innovative application of advanced data mining, data analytics and data visualisation to exploit the growth potential of the UK's leading insight platform for professional services firms
Mondaq Ltd
67% - The design and development of a scalable, avatar based, digital healthcare platform, driven by AI and Machine Learning technology.
Technology STrategy Board
33% - The design and development of a scalable, avatar based, digital healthcare platform, driven by AI and Machine Learning technology.
Orbital Media & Advertising Ltd.
Scoping Exercise for new data product
Hood Group Ltd
2015
67% - To extend the business intelligence and digital marketing offer by developing and embedding a new data analytics capability
Technology STrategy Board
33% - To extend the business intelligence and digitial marketing offer by developing and embedding a new data analytics capability
Objective Computing Ltd